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Recent Progress of the Quasientropy Approach to Signal and Image Processing

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Emerging Intelligent Computing Technology and Applications (ICIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5754))

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Abstract

The quasientropy (QE) is a class of infinitely many functions of probabilities that is similar to the Shannon entropy. In this paper, we review the application of the QE approach to independent component analysis (ICA) and chaotic time series analysis. We also report the new progress of the QE approach to textural features extraction in image processing.

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References

  1. Zemansky, M.W.: Heat and Thermodynamics. McGraw-Hill, New York (1968)

    Google Scholar 

  2. Shannon, C.E.: A Mathematical Theory of Communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)

    MATH  MathSciNet  Google Scholar 

  3. Renyi, A.: On Measures of Entropy and Information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 547–561 (1961)

    Google Scholar 

  4. Csiszar, I.: Information-Type Measures of Difference of Probability Distributions and Indirect Observations. Studia Scientiarum Mathematicarum Hungarica 2, 299–318 (1967)

    MATH  MathSciNet  Google Scholar 

  5. Kapur, J.N.: Measures of Information and Their Applications. John Wiley & Sons, New York (1994)

    MATH  Google Scholar 

  6. Chen, Y.: Blind Separation Using Convex Functions. IEEE Trans. Signal Processing 53(6), 2027–2035 (2005)

    Article  Google Scholar 

  7. Chen, Y.: A Novel Grid Occupancy Criterion for Independent Component Analysis. IEICE Trans. Fundamentals E92-A(8) (2009)

    Google Scholar 

  8. Chen, Y., Aihara, K.: New Results on Criteria for Choosing Delay in Strange Attractor Reconstruction. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS, vol. 5226, pp. 946–953. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Comon, P.: Independent Component Analysis, A New Concept?. Signal Processing 36, 287–314 (1994)

    Article  MATH  Google Scholar 

  10. Jutten, C., Herault, J.: Blind Separation of Sources, Part I: An Adaptive Algorithm Based on Neuromimetic Architecture. Signal Processing 24, 1–10 (1991)

    Article  MATH  Google Scholar 

  11. Yang, H.H., Amari, S.: Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information. Neural Computation 9, 1457–1482 (1997)

    Article  Google Scholar 

  12. Bell, A.J., Sejnowski, T.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)

    Article  Google Scholar 

  13. Fraser, A.M., Swinney, H.L.: Independent Coordinates for Strange Attractors from Mutual Information. Physical Review A 33(2), 1134–1140 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  14. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Systems, Man, and Cybernetics SMC-3(6), 610–621 (1973)

    Google Scholar 

  15. Ohanian, P.P., Dubes, R.C.: Performance Evaluation for Four Classes of Textural Features. Pattern Recognition 25(8), 819–833 (1992)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Yang Chen & Zhimin Zeng

Authors
  1. Yang Chen
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  2. Zhimin Zeng
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, Nam-Ku, 680 - 749, Ulsan, Korea

    Kang-Hyun Jo

  3. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Hong-Hee Lee

  4. School of Electrical Engineering, University of Ulsan, South Korea

    Hee-Jun Kang

  5. e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Str. Prov. per Casamassima Km., 3-70010, Valenzano, (BA), Italy

    Vitoantonio Bevilacqua

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© 2009 Springer-Verlag Berlin Heidelberg

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Chen, Y., Zeng, Z. (2009). Recent Progress of the Quasientropy Approach to Signal and Image Processing. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_40

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  • DOI: https://doi.org/10.1007/978-3-642-04070-2_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04069-6

  • Online ISBN: 978-3-642-04070-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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